Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
NSE Stock Market Prediction Using Deep-Learning Models
2018364 citationsGopalakrishnan E. A, Soman K.P. et al.profile →
A Visualized Botnet Detection System Based Deep Learning for the Internet of Things Networks of Smart Cities
2020222 citationsR. Vinayakumar, Soman K.P. et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Soman K.P.'s research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Soman K.P. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soman K.P. more than expected).
This network shows the impact of papers produced by Soman K.P.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Soman K.P.. The network helps show where Soman K.P. may publish in the future.
Co-authorship network of co-authors of Soman K.P.
This figure shows the co-authorship network connecting the top 25 collaborators of Soman K.P..
A scholar is included among the top collaborators of Soman K.P. based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Soman K.P.. Soman K.P. is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kumar, M. Anand, et al.. (2017). NLP CEN AMRITA SMM4H: Health care text classification through class embeddings. CEUR Workshop Proceedings. 1996.1 indexed citations
12.
Kumar, M. Anand, et al.. (2017). Overview of the INLI PAN at FIRE-2017 Track on Indian Native Language Identification.. 99–105.6 indexed citations
13.
Kumar, M. Anand, et al.. (2017). Distributed Representation in Information Retrieval - AMRITA_CEN_NLP@IRLeD 2017.. 69–71.2 indexed citations
14.
Kumar, M. Anand, et al.. (2017). AmritaNLP@PAN-RusProfiling : Author Profiling using Machine Learning Techniques.. 8–12.3 indexed citations
15.
Kumar, M. Anand, et al.. (2016). Conditional random fields for code mixed Entity Recognition. CEUR Workshop Proceedings. 1737. 309–312.3 indexed citations
16.
Kumar, M. Anand, et al.. (2016). Distributional Semantic Representation for Text Classification and Information Retrieval.. CEUR Workshop Proceedings. 1737. 126–130.3 indexed citations
17.
Kumar, M. Anand, et al.. (2016). DPIL@FIRE2016: Overview of the Shared task on Detecting Paraphrases in Indian language.. 233–238.1 indexed citations
K.P., Soman, et al.. (2013). Singular Value Decomposition A Classroom Approach.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.